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Found 4 papers, 1 papers with code
Enhancing Policy Gradient with the Polyak Step-Size Adaption
Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL).
Fast Convergence of Random Reshuffling under Over-Parameterization and the Polyak-Łojasiewicz Condition
Modern machine learning models are often over-parameterized and as a result they can interpolate the training data.
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus
In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as a constrained program.
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD
The key enabling technique is to interpret MAML as a bilevel optimization (BLO) problem and leverage the sign-based SGD(signSGD) as a lower-level optimizer of BLO.
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